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Bibliographic Details
Main Authors:	Tanoue, Hayato, Nishihara, Hiroki, Suzuki, Yuma, Hori, Takayuki, Takushima, Hiroki, Manojkumar, Aiswariya, Shibata, Yuki, Takeda, Mitsuru, Beppu, Fumika, Hengwei, Zhao, Kanda, Yuto, Yamaga, Daichi
Format:	Preprint
Published:	2025
Subjects:	Computer Vision and Pattern Recognition
Online Access:	https://arxiv.org/abs/2507.08022
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Table of Contents:

This report presents the CuriosAI team's submission to the EgoExo4D Proficiency Estimation Challenge at CVPR 2025. We propose two methods for multi-view skill assessment: (1) a multi-task learning framework using Sapiens-2B that jointly predicts proficiency and scenario labels (43.6 % accuracy), and (2) a two-stage pipeline combining zero-shot scenario recognition with view-specific VideoMAE classifiers (47.8 % accuracy). The superior performance of the two-stage approach demonstrates the effectiveness of scenario-conditioned modeling for proficiency estimation.

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